Papers by Matthew Le
Facebook AI’s WAT19 Myanmar-English Translation Task Submission (D19-52)
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Peng-Jen Chen, Jiajun Shen, Matthew Le, Vishrav Chaudhary, Ahmed El-Kishky, Guillaume Wenzek, Myle Ott, Marc’Aurelio Ranzato
| Challenge: | Using back-translation, we can improve generalization by using noisy channel re-ranking and ensembling. |
| Approach: | They propose to use BPE-based transformer models to leverage monolingual data to improve generalization and use noisy channel re-ranking and ensembling to improve results. |
| Outcome: | The proposed system improves on the baseline system trained exclusively on the provided small parallel dataset, and the human evaluation and BLEU score are higher. |
Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings (P19-1)
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| Challenge: | Using hyperbolic embeddings, we can infer concept hierarchies from distributional contexts while also being able to predict missing “is-a”-relationships and correct wrong extractions. |
| Approach: | They propose a method combining hyperbolic embeddings and Hearst patterns to set appropriate constraints for inferring “is-a” relationships from large text corpora and improve taxonomic consistency. |
| Outcome: | The proposed method achieves state-of-the-art performance on a variety of hypernymy benchmarks. |
The Source-Target Domain Mismatch Problem in Machine Translation (2021.eacl-main)
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Jiajun Shen, Peng-Jen Chen, Matthew Le, Junxian He, Jiatao Gu, Myle Ott, Michael Auli, Marc’Aurelio Ranzato
| Challenge: | Despite the interconnected world we live in, people in different places talk about different things in different parts of the world. |
| Approach: | They propose a metric to quantify the effect of local context in machine translation and propose measurable results. |
| Outcome: | The proposed metric can be used to quantify the effect of local context on the use of language in machine translation systems on low resource languages. |
Revisiting the Evaluation of Theory of Mind through Question Answering (D19-1)
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| Challenge: | Existing benchmarks for theory of mind are flawed due to dataset biases . evaluators have been using the Sally-Anne test to infer false beliefs in others . |
| Approach: | They propose to use question answering to evaluate theory of mind . they propose to explicitly control for data regularities via a careful examination of the answer space . |
| Outcome: | The proposed evaluation protocol and dataset control for data regularities via a careful examination of the answer space. |